




"""simple docstring"""





import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A				=							logging.get_logger(__name__)

A				=							{
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}



class   a__					(						__magic_name__ ):
			lowercase_		=						"conditional_detr"
			lowercase_		=						["past_key_values"]
			lowercase_		=						{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}


			def __init__(   self   :     Tuple   ,	UpperCamelCase_   :     Optional[Any]=True   ,	UpperCamelCase_   :     Union[str, Any]=None   ,	UpperCamelCase_   :     List[str]=3   ,	UpperCamelCase_   :     int=300   ,	UpperCamelCase_   :     List[Any]=6   ,	UpperCamelCase_   :     Optional[int]=2048   ,	UpperCamelCase_   :     str=8   ,	UpperCamelCase_   :     Tuple=6   ,	UpperCamelCase_   :     int=2048   ,	UpperCamelCase_   :     Optional[Any]=8   ,	UpperCamelCase_   :     Optional[int]=0.0   ,	UpperCamelCase_   :     Tuple=0.0   ,	UpperCamelCase_   :     Optional[int]=True   ,	UpperCamelCase_   :     int="relu"   ,	UpperCamelCase_   :     Optional[int]=256   ,	UpperCamelCase_   :     str=0.1   ,	UpperCamelCase_   :     Union[str, Any]=0.0   ,	UpperCamelCase_   :     Any=0.0   ,	UpperCamelCase_   :     Optional[int]=0.02   ,	UpperCamelCase_   :     str=1.0   ,	UpperCamelCase_   :     Any=False   ,	UpperCamelCase_   :     Union[str, Any]="sine"   ,	UpperCamelCase_   :     Tuple="resnet50"   ,	UpperCamelCase_   :     str=True   ,	UpperCamelCase_   :     Any=False   ,	UpperCamelCase_   :     Dict=2   ,	UpperCamelCase_   :     str=5   ,	UpperCamelCase_   :     Tuple=2   ,	UpperCamelCase_   :     str=1   ,	UpperCamelCase_   :     str=1   ,	UpperCamelCase_   :     Optional[int]=2   ,	UpperCamelCase_   :     Tuple=5   ,	UpperCamelCase_   :     Any=2   ,	UpperCamelCase_   :     Optional[int]=0.25   ,	**UpperCamelCase_   :     List[Any]   ,	):


						"""simple docstring"""




						if backbone_config is not None and use_timm_backbone:
									raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.")

						if not use_timm_backbone:
									if backbone_config is None:
												logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
												__UpperCAmelCase						:			Optional[Any]												=       CONFIG_MAPPING["resnet"](out_features=["stage4"])
									elif isinstance(UpperCamelCase_   ,	UpperCamelCase_):
												__UpperCAmelCase						:			int												=       backbone_config.get("model_type")
												__UpperCAmelCase						:			Dict												=       CONFIG_MAPPING[backbone_model_type]
												__UpperCAmelCase						:			int												=       config_class.from_dict(UpperCamelCase_)

						__UpperCAmelCase						:			Optional[Any]												=       use_timm_backbone
						__UpperCAmelCase						:			Optional[int]												=       backbone_config
						__UpperCAmelCase						:			Union[str, Any]												=       num_channels
						__UpperCAmelCase						:			Optional[int]												=       num_queries
						__UpperCAmelCase						:			List[Any]												=       d_model
						__UpperCAmelCase						:			int												=       encoder_ffn_dim
						__UpperCAmelCase						:			Optional[int]												=       encoder_layers
						__UpperCAmelCase						:			Optional[int]												=       encoder_attention_heads
						__UpperCAmelCase						:			List[Any]												=       decoder_ffn_dim
						__UpperCAmelCase						:			Optional[Any]												=       decoder_layers
						__UpperCAmelCase						:			str												=       decoder_attention_heads
						__UpperCAmelCase						:			Union[str, Any]												=       dropout
						__UpperCAmelCase						:			Optional[int]												=       attention_dropout
						__UpperCAmelCase						:			List[Any]												=       activation_dropout
						__UpperCAmelCase						:			Tuple												=       activation_function
						__UpperCAmelCase						:			List[Any]												=       init_std
						__UpperCAmelCase						:			List[str]												=       init_xavier_std
						__UpperCAmelCase						:			Dict												=       encoder_layerdrop
						__UpperCAmelCase						:			Optional[int]												=       decoder_layerdrop
						__UpperCAmelCase						:			int												=       encoder_layers
						__UpperCAmelCase						:			Optional[int]												=       auxiliary_loss
						__UpperCAmelCase						:			Optional[int]												=       position_embedding_type
						__UpperCAmelCase						:			Optional[int]												=       backbone
						__UpperCAmelCase						:			List[Any]												=       use_pretrained_backbone
						__UpperCAmelCase						:			Optional[Any]												=       dilation
						# Hungarian matcher
						__UpperCAmelCase						:			List[str]												=       class_cost
						__UpperCAmelCase						:			Optional[int]												=       bbox_cost
						__UpperCAmelCase						:			Dict												=       giou_cost
						# Loss coefficients
						__UpperCAmelCase						:			Dict												=       mask_loss_coefficient
						__UpperCAmelCase						:			Any												=       dice_loss_coefficient
						__UpperCAmelCase						:			str												=       cls_loss_coefficient
						__UpperCAmelCase						:			int												=       bbox_loss_coefficient
						__UpperCAmelCase						:			str												=       giou_loss_coefficient
						__UpperCAmelCase						:			List[Any]												=       focal_alpha
						super().__init__(is_encoder_decoder=UpperCamelCase_   ,	**UpperCamelCase_)


			@property
			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						return self.encoder_attention_heads


			@property
			def    a_  (   self   :     int):


						"""simple docstring"""




						return self.d_model


			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       copy.deepcopy(self.__dict__)
						if self.backbone_config is not None:
									__UpperCAmelCase						:			int												=       self.backbone_config.to_dict()
						__UpperCAmelCase						:			Tuple												=       self.__class__.model_type
						return output






class   a__					(						__magic_name__ ):
			lowercase_		=						version.parse("1.11" )


			@property
			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						        ("pixel_mask", {0: "batch"}),
						    ])


			@property
			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						return 1e-5


			@property
			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						return 12








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCAmelCase_     						=				{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    lowerCAmelCase_     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


